AI Boilerplates

Explore 58 boilerplates in this collection. Find the perfect starting point for your next project.

Visit website for ShipAhead

ShipAhead

Complete Nuxt 4 boilerplate and launch SaaS in hours

JavaScript
DaisyUI
Markdown
Nuxt
Tailwind CSS
Vue.js
Drizzle ORM
Neon
PostgreSQL
Supabase
Stripe
Nuxt

Features:

Access Control
Admin
AI
Analytics
Animations
API
Auth
+51 more
Visit website for Supastarter

Supastarter

Scalable and production-ready SaaS starter kit for Next.js, Nuxt, and SvelteKit.

JavaScript
TypeScript
Radix UI
Radix Vue
shadcn/ui
Tailwind CSS
Prisma
Chargebee
Creem
Lemon Squeezy
Polar
Stripe
Next.js
Nuxt
React
Svelte
SvelteKit
Vue.js

Features:

Access Control
AI
Analytics
API
Auth
Blog
Contact
+10 more
Visit website for useSAASkit

useSAASkit

The Next.js boilerplate that gives you auth, multi-org, admin tools, billing, marketing pages, analytics, and AI — ready from day one.

JavaScript
TypeScript
Tailwind CSS
Supabase
Lemon Squeezy
Stripe
Next.js

Features:

Access Control
Admin
AI
Analytics
Auth
Blog
Docs
+7 more
Visit website for Gravity

Gravity

The original Node.js & React SaaS boilerplate with subscription billing, authentication, and UI components.

JavaScript
React
shadcn/ui
Amazon Redshift
MariaDB
MongoDB
MSSQL
MySQL
Oracle
PostgreSQL
SQLite
Stripe
Next.js
Node.js
React
React Native

Features:

2FA
Access Control
Admin
AI
API
Auth
Dark Mode
+11 more
Visit website for SwiftyLaunch

SwiftyLaunch

iOS App Generator that handles tedious setup work for developers

Swift
SwiftUI
Firestore
Supabase
In-App Purchases
Firebase
PostHog
SwiftUI

Features:

AI
Analytics
Auth
Backend
Marketing
Notifications
Payments
+1 more
Visit website for AnotherWrapper

AnotherWrapper

10 customizable AI demo apps to build your AI startup in hours

JavaScript
TypeScript
DaisyUI
shadcn/ui
Tailwind CSS
PostgreSQL
Supabase
Lemon Squeezy
Stripe
Next.js
React

Features:

AI
Analytics
Auth
Blog
ChatGPT
Emails
OpenAI
+1 more
Visit website for RockStack

RockStack

The quickest way to build a full-stack SaaS app with Next.js, Remix or SvelteKit.

JavaScript
TypeScript
shadcn/ui
Tailwind CSS
MySQL
PostgreSQL
SQLite
Stripe
Next.js
Remix
Svelte
SvelteKit

Features:

Access Control
AI
Auth
Caching
Emails
i18n
Marketing
+6 more
Visit website for Next Forge

Next Forge

Production-grade Turborepo template for Next.js apps

JavaScript
TypeScript
Radix UI
shadcn/ui
Tailwind CSS
EdgeDB
Neon
Prisma
Turso
Stripe
Next.js
React
Turborepo

Features:

AI
Analytics
API
Auth
Blog
Dark Mode
Docs
+8 more
Visit website for SaaSConstruct

SaaSConstruct

AWS cloud template for building SaaS applications in one day

JavaScript
Python
TypeScript
Vue.js
AWS
Lemon Squeezy
Stripe
AWS CDK
Vue.js

Features:

AI
API
Auth
AWS
Billing
Blog
CI/CD
+9 more

Showing 9 of 58 boilerplates

Why Choose AI Boilerplates?

AI represents a complete full-stack feature with dedicated API endpoints, database models, and UI components architected for SaaS applications. Our boilerplates with AI implement layered architecture patterns—separating business logic, data access, and presentation—with security measures and testing strategies specific to AI's functionality.

AI boilerplates implement full-stack architecture with service layers for business logic, repository patterns for data access, and RESTful/GraphQL API endpoints. They include AI-specific security measures like input validation with schema libraries (Zod, Joi), parameterized queries for SQL injection prevention, and CSRF protection. The implementation handles AI's real-time requirements with WebSockets or SSE when needed, includes comprehensive error handling, and follows OWASP security guidelines for AI's functionality.

Key Benefits

  • AI layered architecture
  • AI-specific security measures
  • AI API endpoint design
  • AI real-time capabilities
  • AI validation schemas
  • AI error handling
  • AI testing suite
  • AI performance optimization

Browse our collection of 58 AI boilerplates to find the perfect starting point for your next SaaS project. Each boilerplate has been carefully reviewed to ensure quality, security, and production-readiness.

Frequently Asked Questions

How is AI architecturally implemented?

AI is implemented following full-stack architecture patterns with dedicated API endpoints, database models with proper relationships, and corresponding UI components. The feature includes its own service layer for business logic, validation schemas, error handling, and event-driven updates. The architecture separates concerns between presentation, business logic, and data access layers, making AI maintainable and testable.

What security measures protect AI?

AI implements defense-in-depth security including input validation with schema validation libraries (Zod, Joi, Yup), parameterized database queries to prevent SQL injection, output encoding to prevent XSS attacks, CSRF token validation, and proper authentication/authorization checks. The feature includes rate limiting, audit logging, and follows OWASP security guidelines specific to AI's functionality.

How does AI handle real-time updates?

AI can include real-time capabilities using WebSockets, Server-Sent Events (SSE), or polling strategies depending on the use case. Real-time implementations use Socket.io, native WebSockets, or framework-specific solutions with proper connection management, authentication, and scaling considerations. The feature handles reconnection logic, message queuing, and optimistic UI updates for responsive user experience.

What API patterns does AI use?

AI's API endpoints follow RESTful principles or GraphQL patterns with proper HTTP methods, status codes, and response structures. The implementation includes request validation, pagination for list endpoints, filtering and sorting capabilities, and comprehensive error responses with meaningful messages. API versioning, rate limiting per endpoint, and OpenAPI/GraphQL schema documentation are included for AI's public-facing endpoints.

How is AI tested and validated?

AI includes unit tests for business logic, integration tests for API endpoints and database interactions, and end-to-end tests for critical user flows. The testing suite uses framework-specific tools (Jest, Pytest, RSpec, PHPUnit) with mocking libraries, test fixtures, and database seeding. Tests cover happy paths, error cases, edge conditions, and security scenarios specific to AI's functionality with proper test coverage reporting.